{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kats 203 Time Series Features\n",
    "\n",
    "This tutorial will introduce `TsFeatures` in Kats, which allows you to extract meaningful features from time series.  The table of contents for Kats 203 is as follows:\n",
    "\n",
    "1. Basic Usage with a Single Time Series        \n",
    "2. Applications with Multiple Time Series        \n",
    "    2.1 Largest Seasonal Component        \n",
    "    2.2 Highest Entropy (\"Least Predictable\")        \n",
    "    2.3 Cluster Similar Time Series        \n",
    "3. Out-in/out features for calculation        \n",
    "    3.1 Opting-out features        \n",
    "    3.2 Opting-in features        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** We provide two types of tutorial notebooks\n",
    "- **Kats 101**, basic data structure and functionalities in Kats (this tutorial)  \n",
    "- **Kats 20x**, advanced topics, including advanced forecasting techniques, advanced detection algorithms, `TsFeatures`, meta-learning, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pprint\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from kats.consts import TimeSeriesData\n",
    "from statsmodels.tsa.seasonal import STL\n",
    "from kats.utils.simulator import Simulator\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from kats.tsfeatures.tsfeatures import TsFeatures\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purposes of this tutorial, we are going to use the `Simulator` to generate a list of 30 different `TimeSeriesData` objects called `ts_list`.  It contains 10 time series simulated from the ARIMA model, 10 time series with trend shifts, and 10 time series with level shifts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Simulator(n=90, freq=\"D\", start = \"2021-01-01\") # simulate 90 days of data\n",
    "random_seed = 100\n",
    "\n",
    "# generate 10 TimeSeriesData with arima_sim\n",
    "np.random.seed(random_seed) # setting numpy seed\n",
    "arima_sim_list = [sim.arima_sim(ar=[0.1, 0.05], ma = [0.04, 0.1], d = 1) for _ in range(10)]\n",
    "\n",
    "# generate 10 TimeSeriesData with trend shifts\n",
    "trend_sim_list = [\n",
    "    sim.trend_shift_sim(\n",
    "        cp_arr = [30, 60, 75],\n",
    "        trend_arr=[3, 15, 2, 8],\n",
    "        intercept=30,\n",
    "        noise=50,\n",
    "        seasonal_period=7,\n",
    "        seasonal_magnitude=np.random.uniform(10, 100),\n",
    "        random_seed=random_seed\n",
    "    ) for _ in range(10)\n",
    "]\n",
    "\n",
    "\n",
    "# generate 10 TimeSeriesData with level shifts\n",
    "level_shift_list = [\n",
    "    sim.level_shift_sim(\n",
    "        cp_arr = [30, 60, 75],\n",
    "        level_arr=[1.35, 1.05, 1.35, 1.2],\n",
    "        noise=0.05,\n",
    "        seasonal_period=7,\n",
    "        seasonal_magnitude=np.random.uniform(0.1, 1.0),\n",
    "        random_seed=random_seed\n",
    "    ) for _ in range(10)\n",
    "]\n",
    "\n",
    "ts_list = arima_sim_list + trend_sim_list + level_shift_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Basic Usage with a Single Time Series\n",
    "We begin by introducing the basic usage of `TsFeatures`.  For the purposes of this example, we will use the simulator to simulate a bunch of different time series from the ARIMA model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`TsFeatures` currently can only process one time series a time, so let's start by taking a look at the first time series we generated above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = ts_list[0]\n",
    "\n",
    "# plot the time series\n",
    "ts.plot(cols=['value'])\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting the basic features from the time series using `TsFeatures` is straightforward.  We can do so as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.41641147078333146,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.8346355269096618,\n",
       " 'trend_strength': 0.9853025999592567,\n",
       " 'seasonality_strength': 0.3521955818150291,\n",
       " 'spikiness': 0.00020455870537077636,\n",
       " 'peak': 1,\n",
       " 'trough': 6,\n",
       " 'level_shift_idx': 23,\n",
       " 'level_shift_size': 0.7134342301151566,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.03034496255047374,\n",
       " 'firstmin_ac': 53,\n",
       " 'firstzero_ac': 30,\n",
       " 'holt_alpha': 0.9999999850969992,\n",
       " 'holt_beta': nan,\n",
       " 'hw_alpha': nan,\n",
       " 'hw_beta': nan,\n",
       " 'hw_gamma': nan}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1. initiate TsFeatures\n",
    "model = TsFeatures()\n",
    "\n",
    "# Step 2. use .transform() method, and apply on the target time series data\n",
    "output_features = model.transform(ts)\n",
    "output_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dictionary above shows 40 features, which are the features that we calculate by default.  There are 28 additional features that we support in `TsFeatures`, and users can select which features they would like to include in their calculations using the `selected_features` argument.  We will see an example of this later.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Applications with Multiple Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will look at each of the time series in `ts_list` and use `TsFeatures` to calculate the features for each of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = TsFeatures()\n",
    "output_features = [model.transform(ts) for ts in ts_list] # loop through time series data and perform transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view the results as a `DataFrame` as follows, which has one row for each time series in `ts_list` and each column represents a different feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>entropy</th>\n",
       "      <th>lumpiness</th>\n",
       "      <th>stability</th>\n",
       "      <th>flat_spots</th>\n",
       "      <th>hurst</th>\n",
       "      <th>std1st_der</th>\n",
       "      <th>crossing_points</th>\n",
       "      <th>...</th>\n",
       "      <th>diff2y_pacf5</th>\n",
       "      <th>seas_acf1</th>\n",
       "      <th>seas_pacf1</th>\n",
       "      <th>firstmin_ac</th>\n",
       "      <th>firstzero_ac</th>\n",
       "      <th>holt_alpha</th>\n",
       "      <th>holt_beta</th>\n",
       "      <th>hw_alpha</th>\n",
       "      <th>hw_beta</th>\n",
       "      <th>hw_gamma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>-4.973228</td>\n",
       "      <td>50.694998</td>\n",
       "      <td>0.274245</td>\n",
       "      <td>10.258210</td>\n",
       "      <td>45.077604</td>\n",
       "      <td>1</td>\n",
       "      <td>0.418844</td>\n",
       "      <td>0.877359</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.361458</td>\n",
       "      <td>0.814998</td>\n",
       "      <td>0.030345</td>\n",
       "      <td>53</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90</td>\n",
       "      <td>-2.058767</td>\n",
       "      <td>10.041589</td>\n",
       "      <td>0.366121</td>\n",
       "      <td>23.888037</td>\n",
       "      <td>5.711696</td>\n",
       "      <td>1</td>\n",
       "      <td>0.449241</td>\n",
       "      <td>0.701685</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.749517</td>\n",
       "      <td>0.327535</td>\n",
       "      <td>-0.226641</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>0.866466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>9.908474</td>\n",
       "      <td>54.513387</td>\n",
       "      <td>0.371858</td>\n",
       "      <td>44.741126</td>\n",
       "      <td>45.977900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.249448</td>\n",
       "      <td>0.983165</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.324193</td>\n",
       "      <td>0.668572</td>\n",
       "      <td>0.124300</td>\n",
       "      <td>55</td>\n",
       "      <td>31</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90</td>\n",
       "      <td>-4.852956</td>\n",
       "      <td>21.728208</td>\n",
       "      <td>0.492625</td>\n",
       "      <td>9.067492</td>\n",
       "      <td>16.945737</td>\n",
       "      <td>1</td>\n",
       "      <td>0.377345</td>\n",
       "      <td>0.705047</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.533415</td>\n",
       "      <td>0.524764</td>\n",
       "      <td>0.009480</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>-1.521848</td>\n",
       "      <td>28.601801</td>\n",
       "      <td>0.402300</td>\n",
       "      <td>259.161089</td>\n",
       "      <td>17.962647</td>\n",
       "      <td>1</td>\n",
       "      <td>0.543165</td>\n",
       "      <td>0.880529</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.460363</td>\n",
       "      <td>0.398363</td>\n",
       "      <td>-0.106916</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   length      mean        var   entropy   lumpiness  stability  flat_spots  \\\n",
       "0      90 -4.973228  50.694998  0.274245   10.258210  45.077604           1   \n",
       "1      90 -2.058767  10.041589  0.366121   23.888037   5.711696           1   \n",
       "2      90  9.908474  54.513387  0.371858   44.741126  45.977900           1   \n",
       "3      90 -4.852956  21.728208  0.492625    9.067492  16.945737           1   \n",
       "4      90 -1.521848  28.601801  0.402300  259.161089  17.962647           1   \n",
       "\n",
       "      hurst  std1st_der  crossing_points  ...  diff2y_pacf5  seas_acf1  \\\n",
       "0  0.418844    0.877359                5  ...      0.361458   0.814998   \n",
       "1  0.449241    0.701685                8  ...      0.749517   0.327535   \n",
       "2  0.249448    0.983165                3  ...      0.324193   0.668572   \n",
       "3  0.377345    0.705047               11  ...      0.533415   0.524764   \n",
       "4  0.543165    0.880529                6  ...      0.460363   0.398363   \n",
       "\n",
       "   seas_pacf1  firstmin_ac  firstzero_ac  holt_alpha  holt_beta  hw_alpha  \\\n",
       "0    0.030345           53            30    1.000000        NaN       NaN   \n",
       "1   -0.226641           21            11    0.866466        NaN       NaN   \n",
       "2    0.124300           55            31    1.000000        NaN       NaN   \n",
       "3    0.009480           31            27    1.000000        NaN       NaN   \n",
       "4   -0.106916           25            16    1.000000        NaN       NaN   \n",
       "\n",
       "   hw_beta  hw_gamma  \n",
       "0      NaN       NaN  \n",
       "1      NaN       NaN  \n",
       "2      NaN       NaN  \n",
       "3      NaN       NaN  \n",
       "4      NaN       NaN  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features = pd.DataFrame(output_features) # converting to dataframe\n",
    "df_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will take a look at some of the applications of the features we have just calculated for each each of the time series in `TsFeatures`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Largest Seasonal Component\n",
    "We will demonstrate leveraging `TsFeatures` for finding the time series data with the highest seasonality strength among the simulated time series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding the index of the time series sample with the highest seasonality strength\n",
    "index_target_ts = df_features['seasonality_strength'].argmax() \n",
    "\n",
    "target_ts = ts_list[index_target_ts] \n",
    "\n",
    "# Plot the time series\n",
    "target_ts.plot(cols=['value'])\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a look at the STL decomposition of this time series to see its seasonal component.  We use the [`STL` module from `statsmodels`](https://www.statsmodels.org/devel/generated/statsmodels.tsa.seasonal.STL.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stl = STL(target_ts.value.values, period=7)\n",
    "res = stl.fit()\n",
    "plt.plot(\n",
    "    pd.to_datetime(target_ts.time.values),\n",
    "    res.seasonal\n",
    ")\n",
    "plt.xticks(rotation = 90);\n",
    "plt.title(f'Seasonal component - variance: {np.round(np.var(res.seasonal), 2)}');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we repeat the process for the time series in `ts_list` with the smallest seasonal component.  We see that the variance of the seasonal components is much smaller in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding the index of the time series sample with the smallest seasonality strength\n",
    "index_target_ts = df_features['seasonality_strength'].argmin() \n",
    "target_ts = ts_list[index_target_ts].to_dataframe() \n",
    "\n",
    "# Do an STL decomposition and plot the results\n",
    "stl = STL(target_ts.value.values, period=7)\n",
    "res = stl.fit()\n",
    "plt.plot(\n",
    "    pd.to_datetime(target_ts.time.values),\n",
    "    res.seasonal\n",
    ")\n",
    "plt.xticks(rotation = 45);\n",
    "plt.title(f'Seasonal component - variance: {np.round(np.var(res.seasonal), 2)}');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Highest Entropy (\"Least Predictable\")\n",
    "\n",
    "We can intuitively understand entropy as the measure of the disorder of a time series.  In general, a time series with higher entropy will be harder to forecast.  Here, we show how to use `TsFeatures` to find the time series in `ts_list` with the highest entropy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Re-run these cells once we have fixed the definition of entropy in TsFeatures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the index of the time series sample with the highest entropy\n",
    "index_target_ts = df_features['entropy'].argmax() \n",
    "\n",
    "target_ts = ts_list[index_target_ts] \n",
    "\n",
    "# Plot the time series\n",
    "target_ts.plot(cols=['value'])\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the above figure with the time series data with the lowest entropy identified by `TsFeatures`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the index of the time series sample with the lowest entropy\n",
    "index_target_ts = df_features['entropy'].argmin() \n",
    "target_ts = ts_list[index_target_ts]\n",
    "\n",
    "# Plot the time series\n",
    "target_ts.plot(cols=['value'])\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the figures above, this second plot shows a time series with a clear change point and two distinct trends, suggesting it is easier to forecast than the first time series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Cluster Similar Time Series\n",
    "\n",
    "Here we are going to use the features we get from `TsFeatures` to try to cluster the time series.  \n",
    "\n",
    "Let's perform a dimension reduction on the simulated time series data, and visualize to see if there's clear pattern of clusters.  In this example, we pick 5 features to use for each time series, and then we use `PCA` (combined with `StandardScaler`) from `sklearn` to project this representation into two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performing dimension reduction on the time series samples\n",
    "ls_features = ['lumpiness', 'entropy', 'seasonality_strength', 'stability', 'level_shift_size']\n",
    "df_dataset = df_features[ls_features]\n",
    "x_2d = PCA(n_components=2).fit_transform(X=StandardScaler().fit_transform(df_dataset[ls_features].values))\n",
    "df_dataset['pca_component_1'] = x_2d[:,0]\n",
    "df_dataset['pca_component_2'] = x_2d[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the results below.  We have color-coded the different types of simulated time series (ARIMA, trend shift, level shift) and we can see that series of the same type are closer to each other.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the PCA projections of each time series\n",
    "plt.figure(figsize = (15,8))\n",
    "# Plot ARIMA time series in red\n",
    "ax = df_dataset.iloc[0:10].plot(x='pca_component_1', y='pca_component_2', kind='scatter', color='red')\n",
    "# Plot trend shift time series in green\n",
    "df_dataset.iloc[10:20].plot(x='pca_component_1', y='pca_component_2', kind='scatter', color='green', ax=ax)\n",
    "# Plot level shift time series in yellow\n",
    "df_dataset.iloc[20:].plot(x='pca_component_1', y='pca_component_2', kind='scatter', color='yellow', ax=ax)\n",
    "\n",
    "plt.title('Visualization of the dimension reduced time series samples')\n",
    "plt.legend(['ARIMA', 'Trend Shift', 'Level Shift'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Out-in/out features for calculation\n",
    "\n",
    "In `TsFeatures`, you can choose which features and feature groups you would like to calculate and which you would like to skip.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Opting-out features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by returning to our initial example, where we calculated the first time series in `ts_list`.  This calculates the 40 features that we calculate by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.41641147078333146,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.8346355269096618,\n",
       " 'trend_strength': 0.9853025999592567,\n",
       " 'seasonality_strength': 0.3521955818150291,\n",
       " 'spikiness': 0.00020455870537077636,\n",
       " 'peak': 1,\n",
       " 'trough': 6,\n",
       " 'level_shift_idx': 23,\n",
       " 'level_shift_size': 0.7134342301151566,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.03034496255047374,\n",
       " 'firstmin_ac': 53,\n",
       " 'firstzero_ac': 30,\n",
       " 'holt_alpha': 0.9999999850969992,\n",
       " 'holt_beta': nan,\n",
       " 'hw_alpha': nan,\n",
       " 'hw_beta': nan,\n",
       " 'hw_gamma': nan}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = ts_list[0]\n",
    "TsFeatures().transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can omit a single feature, like `seasonality_strength`, by passing `seasonality_strength=False` into `TsFeatures` like such."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.41641147078333146,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.8346355269096618,\n",
       " 'trend_strength': 0.9853025999592567,\n",
       " 'spikiness': 0.00020455870537077636,\n",
       " 'peak': 1,\n",
       " 'trough': 6,\n",
       " 'level_shift_idx': 23,\n",
       " 'level_shift_size': 0.7134342301151566,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.03034496255047374,\n",
       " 'firstmin_ac': 53,\n",
       " 'firstzero_ac': 30,\n",
       " 'holt_alpha': 0.9999999850969992,\n",
       " 'holt_beta': nan,\n",
       " 'hw_alpha': nan,\n",
       " 'hw_beta': nan,\n",
       " 'hw_gamma': nan}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(seasonality_strength=False).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `seasonality_strength` feature belongs to the 'stl_features' feature group.  Each of the 68 features that we currently support in `TsFeatures` are arranged into 14 feature groups.  Below is the dictionary displaying the mapping feature feature groups and features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acfpacf_features': ['y_acf1',\n",
      "                      'y_acf5',\n",
      "                      'diff1y_acf1',\n",
      "                      'diff1y_acf5',\n",
      "                      'diff2y_acf1',\n",
      "                      'diff2y_acf5',\n",
      "                      'y_pacf5',\n",
      "                      'diff1y_pacf5',\n",
      "                      'diff2y_pacf5',\n",
      "                      'seas_acf1',\n",
      "                      'seas_pacf1'],\n",
      " 'bocp_detector': ['bocp_num', 'bocp_conf_max', 'bocp_conf_mean'],\n",
      " 'cusum_detector': ['cusum_num',\n",
      "                    'cusum_conf',\n",
      "                    'cusum_cp_index',\n",
      "                    'cusum_delta',\n",
      "                    'cusum_llr',\n",
      "                    'cusum_regression_detected',\n",
      "                    'cusum_stable_changepoint',\n",
      "                    'cusum_p_value'],\n",
      " 'holt_params': ['holt_alpha', 'holt_beta'],\n",
      " 'hw_params': ['hw_alpha', 'hw_beta', 'hw_gamma'],\n",
      " 'level_shift_features': ['level_shift_idx', 'level_shift_size'],\n",
      " 'nowcasting': ['nowcast_roc',\n",
      "                'nowcast_ma',\n",
      "                'nowcast_mom',\n",
      "                'nowcast_lag',\n",
      "                'nowcast_macd',\n",
      "                'nowcast_macdsign',\n",
      "                'nowcast_macddiff'],\n",
      " 'outlier_detector': ['outlier_num'],\n",
      " 'robust_stat_detector': ['robust_num', 'robust_metric_mean'],\n",
      " 'seasonalities': ['seasonal_period',\n",
      "                   'trend_mag',\n",
      "                   'seasonality_mag',\n",
      "                   'residual_std'],\n",
      " 'special_ac': ['firstmin_ac', 'firstzero_ac'],\n",
      " 'statistics': ['length',\n",
      "                'mean',\n",
      "                'var',\n",
      "                'entropy',\n",
      "                'lumpiness',\n",
      "                'stability',\n",
      "                'flat_spots',\n",
      "                'hurst',\n",
      "                'std1st_der',\n",
      "                'crossing_points',\n",
      "                'binarize_mean',\n",
      "                'unitroot_kpss',\n",
      "                'heterogeneity',\n",
      "                'histogram_mode',\n",
      "                'linearity'],\n",
      " 'stl_features': ['trend_strength',\n",
      "                  'seasonality_strength',\n",
      "                  'spikiness',\n",
      "                  'peak',\n",
      "                  'trough'],\n",
      " 'trend_detector': ['trend_num', 'trend_num_increasing', 'trend_avg_abs_tau']}\n"
     ]
    }
   ],
   "source": [
    "feature_group_mapping = TsFeatures().feature_group_mapping\n",
    "pprint.pprint(feature_group_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can opt to skip the calculation of an entire feature group like `stl_features` the same way we skipped the calculation of `seasonality_strength` in the example above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.41641147078333146,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.8346355269096618,\n",
       " 'level_shift_idx': 23,\n",
       " 'level_shift_size': 0.7134342301151566,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.03034496255047374,\n",
       " 'firstmin_ac': 53,\n",
       " 'firstzero_ac': 30,\n",
       " 'holt_alpha': 0.9999999850969992,\n",
       " 'holt_beta': nan,\n",
       " 'hw_alpha': nan,\n",
       " 'hw_beta': nan,\n",
       " 'hw_gamma': nan}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(stl_features=False).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also opt-out combinations of features and feature groups like su"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.41641147078333146,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.8346355269096618,\n",
       " 'level_shift_idx': 23,\n",
       " 'level_shift_size': 0.7134342301151566,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.03034496255047374,\n",
       " 'firstmin_ac': 53,\n",
       " 'firstzero_ac': 30,\n",
       " 'holt_alpha': 0.9999999850969992,\n",
       " 'holt_beta': nan,\n",
       " 'hw_alpha': nan,\n",
       " 'hw_beta': nan,\n",
       " 'hw_gamma': nan}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsf = TsFeatures(length = False, mean = False, stl_features=False)\n",
    "tsf.transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Opting-in features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `selected_features` argument in `TsFeatures` to specify which features and feature groups we would like to include in our calculations.  When we use this argument, a default feature will not be included unless that feature or its group is explicited included in `selected_features`.  Similarly, a feature not included by default can be included by including it or its group in `selected_features`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example where we specify a list of features to calculate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'trend_strength': 0.9853025999592567,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'firstmin_ac': 53,\n",
       " 'holt_alpha': 0.9999999850969992,\n",
       " 'bocp_num': 2,\n",
       " 'nowcast_roc': 0.05212172487338159,\n",
       " 'seasonality_mag': 1.0}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(selected_features = [\n",
    "    'mean',\n",
    "    'var',\n",
    "    'entropy',\n",
    "    'lumpiness',\n",
    "    'hurst',\n",
    "    'trend_strength',\n",
    "    'y_acf1',\n",
    "    'firstmin_ac',\n",
    "    'holt_alpha',\n",
    "    'nowcast_roc',\n",
    "    'bocp_num',\n",
    "    'seasonality_mag',\n",
    "]).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example where we specify a list of features groups to calculate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.41641147078333146,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.8346355269096618,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.03034496255047374,\n",
       " 'nowcast_roc': 0.05212172487338159,\n",
       " 'nowcast_ma': -4.832882514728336,\n",
       " 'nowcast_mom': -0.9575428298227386,\n",
       " 'nowcast_lag': -4.39332762856868,\n",
       " 'nowcast_macd': -0.9380868822690598,\n",
       " 'nowcast_macdsign': -1.041332210136711,\n",
       " 'nowcast_macddiff': -0.049750365563813244}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(selected_features = [\n",
    "    'statistics',\n",
    "    'acfpacf_features',\n",
    "    'nowcasting',\n",
    "]).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can specify a combination of features and feature groups to calculate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.41641147078333146,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.8346355269096618,\n",
       " 'seasonality_strength': 0.3521955818150291,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.03034496255047374,\n",
       " 'nowcast_roc': 0.05212172487338159,\n",
       " 'nowcast_ma': -4.832882514728336,\n",
       " 'nowcast_mom': -0.9575428298227386,\n",
       " 'nowcast_lag': -4.39332762856868,\n",
       " 'nowcast_macd': -0.9380868822690598,\n",
       " 'nowcast_macdsign': -1.041332210136711,\n",
       " 'nowcast_macddiff': -0.049750365563813244}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(selected_features = [\n",
    "    'statistics',\n",
    "    'acfpacf_features',\n",
    "    'nowcasting',\n",
    "    'seasonality_strength',\n",
    "]).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we can mix up the opt-in and opt-out to calculate majority of the features among some feature groups, while opting-out some of the features within these feature groups that are opt-in.\n",
    "\n",
    "We can also opt-out specific features from the feature groups we include in `selected_features`.  Here is an example where we calculate all of the features in the `statistics` group except for the `mean`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.41641147078333146,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.8346355269096618}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(selected_features = ['statistics'], mean = False).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we include the name of a specific feature in `selected_features` and then try to opt-out that same feature or its feature group, we will get an error.  For example, the inverse of our previous example doesn't work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# THIS DOES NOT WORK\n",
    "# TsFeatures(selected_features = ['mean'], statitics = False).transform(ts)\n",
    "\n",
    "# THIS ALSO DOES NOT WORK\n",
    "# TsFeatures(selected_features = ['mean'], mean = False).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this tutorial, we've demonstrated basic functions of `TsFeatures`, along with the demonstration of some of the interesting applications. We hope you've enjoyed the tutorial and look forward to using `TsFeatures` in the future!"
   ]
  }
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